Neural Network Approach for Non-Markovian Dissipative Dynamics of Many-Body Open Quantum Systems

Simulating the dynamics of open quantum systems coupled to non-Markovian environments remains an outstanding challenge due to exponentially scaling computational costs. We present an artificial intelligence strategy to overcome this obstacle by integrating the neural quantum states approach into the dissipaton-embedded quantum master equation in second quantization (DQME-SQ). Our approach utilizes restricted Boltzmann machines (RBMs) to compactly represent the reduced density tensor, explicitly encoding the combined effects of system-environment correlations and nonMarkovian memory. Applied to model systems exhibiting prominent effects of system-environment correlation and non-Markovian memory, our approach achieves comparable accuracy to conventional hierarchical equations of motion, while requiring significantly fewer dynamical variables. The novel RBM-based DQME-SQ approach paves the way for investigating non-Markovian open quantum dynamics in previously intractable regimes, with implications spanning various frontiers of modern science.

Towards Personalized Management of Type B Aortic Dissection Using STENT: a STandard cta database with annotation of the ENtire aorta and True-false lumen

Type B Aortic Dissection(TBAD) is a rare aortic disease with a high 5-year mortality.Personalized and precise management of TBAD has been increasingly desired in clinic which requires the geometric parameters of TBAD specific to the patient be measured accurately.This remains to be a challenging task for vascular surgeons as manual measurement is highly subjective and imprecise. To solve this problem,we introduce STENT-a STandard cta database with annotation of the ENtire aorta and True-false lumen. The database contains 274 CT angiography (CTA) scans from 274 unique TBAD patients and is split into a training set(254 cases including 210 preoperative and 44 postoperative scans ) and a test set(20 cases).Based on STENT,we develop a series of methods including automated TBAD segmentation and automated measurement of TBAD parameters that facilitate personalized and precise management of the disease. In this work, the database and the proposed methods are thoroughly introduced and evaluated and the results of our study shows the feasibility and effectiveness of our approach to easing the decision-making process for vascular surgeons during personalized TBAD management.

Multi-Task Deep Convolutional Neural Network for the Segmentation of Type B Aortic Dissection

Type B aortic dissection (TBAD) is a rare but life threatening disease. Segmentation of the entire aorta and truefalse lumen is crucial for the planning and follow-up of endovascular repair of TBAD. Manual segmentation in a slice-wise manner is time-consuming and requires expert experience. Current computer-aided methods have several limitations like focusing only on a specific part oftheaorta atatimeorrequiringhumaninteraction. Mostimportantly, these methods can not segment the entire aorta and detect true-false lumen at the same time. We report in this study a fully automatic approach based on multi-task deep convolutional neural network that segments the entire aorta and true-false lumen fromCTA images in a unified framework. Fortraining,webuiltadatabasecontaining254CTA images from both pre-operative and post-operative TBAD patients. These images are from multiple manufacturers. Slice-wise manual segmentation of the entire aorta and the true-false lumen for each 3-D CTA image is also provided. Our method is evaluated on 16 CTA data (11 preoperative and 5 postoperative) whose ground truth segmentation is provided by experienced vascular surgeons.Resultsshow that our method can segment type B aortic dissection with robustness and accuracy. Furthermore,our method can be easily extended to the segmentation of the entire aorta without dissection.